Online modeling of the LiFePO4 power battery based on the data supervisory mechanism

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Impact Factor:9.4

DOI number:10.1016/j.est.2023.108359

Journal:Journal of Energy Storage

Key Words:LiFePO4 power battery, Forgetting Factor Recursive Least Squares, Data supervisory mechanism, Online modeling method

Abstract:The establishment of the accurate lithium-ion power battery model is an important basis to realize the reliable state estimation of the lithium-ion power battery, and also a necessary work to develop the battery management system. However, the existing modeling algorithms lack the data supervision mechanism for the online modeling, and cannot guarantee the stability and clear physical meaning of the model parameters used in the Battery Management System (BMS), which may lead to the breakdown of the BMS state estimation algorithm and major security risks. Therefore, the data supervisory mechanism is designed to solve the problem and a lithium-ion power battery online modeling method based on it is proposed in this paper. Experimental results on the LiFePO4 power battery demonstrate the effective of the proposed online modeling method and lay a foundation for the subsequent state estimation of the LiFePO4 power battery. As the ambient temperature increases from 10℃ to 40℃, the average value and the standard deviation of the LiFePO4 power battery ohmic internal resistance decrease 25.88% and 83.33% respectively. Meanwhile, the Mean Absolutely Error (MAE), the Root Mean Square Error (RMSE) and the Maximum Absolute Error (Max-AE) of the terminal voltage decrease 31.91%, 30.43% and 58.06% respectively.

Note:中科院2区Top

Co-author:Ji Wu

First Author:Muyao Wu

Indexed by:Journal paper

Correspondence Author:Li Wang

Document Code:108359

Discipline:Engineering

Document Type:J

Volume:72

ISSN No.:2352-152X

Translation or Not:no

Date of Publication:2023-07-14

Included Journals:SCI、EI

Links to published journals:https://www.sciencedirect.com/science/article/pii/S2352152X23017565